Data Science and Digital Transformation: Sharing the same Mission
Data Science is critical to Digital Transformation; in fact, the goals of Digital Transformation are the same as the goals for leveraging value from data, as follows:
Improve Customer Experience (CX) and generate goodwill
The vision of each provider at the end of Digital Transformation is to have a 360o view of each customer, enabling them to engage with customers at every touchpoint – across marketing, commerce and service areas, to orchestrate the entire customer journey in a seamless manner. In other words, the purpose is to put the customer at the heart of operations quicker and cost effectively.
A data driven approach for customers will allow companies to listen better based on their own data and third party data, learn faster about customers (history, interests, behavior, preferences, profile) and satisfy smarter (personalized content, product and price strategy, programmatic advertising, next best offer).
Enable more efficient operations and processes (Operational Efficiency)
Almost every business in any market can benefit from digital-driven operational improvements that create greater value. Some of the benefits that operational efficiency can deliver include: faster and more efficient business processes; increased organization agility; reduced costs; improved safety, quality, and productivity.
Operation function can already use data science to unlock operational efficiencies – increasing productivity, extending asset life spans and reducing operating and maintenance costs. Planning function can use data and analytics to create much stronger alignment between supply and demand and improve the overall effectiveness of the planning process. Regulatory function can use data science to better govern and oversee their domain/industry level of compliance.
Build and enhance products and services
In their journey to digital transformation, organizations put a lot of effort and energy to innovate their products and services offering, to better fit customer needs and expectations and thus to derive additional revenues.
Data contributes not only to a better customer experience, but with a similar approach and logic, data will bring additional value for companies’ offerings. Once a company has a 360o view on customer needs, it should also be able to build ‘smart’ offering and incorporate intelligence in its products and services; such ‘smart’ enhancements are nothing else than the result of data science process.
Create new channels and business models
Another objective in digital transformation is to identify new channels (e.g. where people spend more of their time) to address the market and the new generations of consumers. Also, the ability to identify new business models is key for mature companies, in order to adapt to market developments or for startups to accelerate their development in the new digital era.
Once data insights are identified, new revenue streams can be derived through Data as a Service (DaaS) offering – monetize value-added data to third parties (B2B) and using analytics to up-sell and cross-sell (B2B2C) more effectively. However, a more powerful approach is to have a new business model once the journey towards data-driven desiderate comes to an advanced maturity level. Thus, data-driven companies can reuse all internal expertise and provide to third parties the know-how (professional services) and tools as a specialized provider (new line of business inside the organization).
The first two goals above sit under the umbrella of Digital Operation, either customer experience or related to internal processes and organization.
The last two goals sit under the umbrella of Digital Production, namely enhanced products and services, new channels and new business models.
In conclusion, data science literacy is a key element in the digital transformation journey, therefore, the value generated from a data driven approach has a digital taste and texture.